The RNN with any temporal loss was trained followed the setup in [1]}. First, the RNN was initialized with the SFN trained in the first step. The first convolutional layer of the RNN that had six input channels was learned from scratch. Second, the RNN was finetuned with a batch size of 4 according to [1]}. Four consecutive frames formed a minibatch. The other training settings were the same as the SFN. The finetuning step took approximately one hour for different temporal losses.
